Purpose :
Diabetic retinopathy (DR) is one the most common causes of blindness in the western world. Microaneuryms are the hallmark of diabetic retinopathy and are almost universally present in early DR. We present an algorithm for the automatic detection of pathologic capillary dilation and looping as an aide in the diagnosis of microaneurysms.

Methods :
Patients were enrolled at the New England Eye Center at Tufts Medical Center in Boston. Optical coherence tomography angiography (OCTA) data were acquired from 8 patients with varying degrees of DR using the OptoVue Avanti device. 16 OCTA volumes with field sizes of 3x3 mm were acquired in each imaging session. The Avanti software was used to segment the retinal layers end generate en face projections. The en face projections of the superficial and deep capillary plexi were used for the training of the proposed algorithm. A standard machine learning algorithm (random forest) was used to train the automatic detection of capillary dilation in superficial and deep layers based on features in the projections. The training data were generated by an expert grader at the Boston Imaging Reading Center and vascular anomalies such as dilation and looping were labeled. Of the 16 data sets, 5 were selected at random as test sets.

Results :
Of the test data, 99% of all pixels are correctly labeled by the algorithm as either vascular dilation or normal. Figure 1 shows an example detection result from the superficial capillary layer. Details are provided in the figure caption.

Conclusions :
This novel approach suggests that it is possible to fully automate the detection of vascular abnormalities which may correlate to microaneurysms and thus early stages of diabetic retinopathy. A fully automated detection of this type of vascular anomaly has not been demonstrated yet to the best of our knowledge.

This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.

Example image of vessel dilation detection from an en face image of the superficial capillary layer of an eye with PDR. Original en face image is on the left, detected areas on the right. Areas classified by the algorithm as dilated vessels are marked in red. The green circles enclose correctly detected areas of vessel abnormality. The blue circles enclose incorrect detections, one false positive and one false negative.

Example image of vessel dilation detection from an en face image of the superficial capillary layer of an eye with PDR. Original en face image is on the left, detected areas on the right. Areas classified by the algorithm as dilated vessels are marked in red. The green circles enclose correctly detected areas of vessel abnormality. The blue circles enclose incorrect detections, one false positive and one false negative.